Department of Electrical Engineering
Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/18
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Item Economic Feasibility Assessment of a Rural Hybrid Microgrid for Renala Khurd in Pakistan(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Misbah Sattar; FA19-REE-003; Dr. Sobia Baig; LHR TP 8473Reliable access to electric power in rural communities is challenging for the transmission and distribution system of Pakistan. The extension of the existing power system is a challenge due to huge investment cost. One solution is to involve microgrid technology for access of electric power in such communities. Thus, the integration of renewable energy resources at the currently installed power plants is a feasible economic option for Pakistan. Moreover, for reliable operation, these microgrids are supported with battery storage. However, battery storage increases the overall capital and operational cost which in case of rural communities of developing countries are at time unaffordable and practically unsustainable. Pakistan is a country that encompasses a large canal system across the country. Besides, its prime utilization in irrigation, these canals can be beneficial for small-scale hydro power generation. For additional power generation, other renewable sources, such as solar PV with grid backup can be applied. Furthermore, while adding hydro resources, the impact on project performance parameters such as Net Present Cost (NPC), Grid unit purchased, Capital cost and Cost of Energy (COE) also need quantitative assessment with the variation in the system sizing. In this study, a canal hydro source at Renala Khurd Pakistan is used as a case study to measure the quantitative impact on sizing and performance parameters with multiple combinations of the resources. HOMER Pro software has been used to assess sizing and associated economic performance parameters. Meanwhile, for the comparison of HOMER Pro software, Genetic Algorithm (GA) based Objective Function has been developed, keeping in view the required constraints. Results show that, the inclusion of small-scale hydel power as an assisting source with solar and grid combination significantly reduces the sizing of solar PV from 4.7 MW to 0.88 MW and grid unit purchased was reduced to 47% compared to 59%. Key Words: Small-scale hydel (SSH), solar PV (SPV), Genetic Algorithm (GA), Battery storage (BATT), Hybrid Optimization Model for Electric Renewables (HOMER)Item Human Activity Recognition Using Photoplethysmographic Data(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Munief Hassan Tahir; FA19-REE-013; Dr. Khurram Ali; LHR TP 8069Accurately recognizing human activities is a challenging task with numerous potential applications, including fitness tracking and healthcare monitoring. In this study, we used Photoplethysmographic (PPG) sensor data to classify seven different activities performed during fitness training or in a gym setting. The dataset was obtained from two publicly available sources and a combined dataset was generated which consisted of PPG data only. We trained three models on the dataset and achieved an overall performance of 91% in activity classification. The Inception-v3 model slightly outperformed the other two models, which were based on the Inception-ResNet-v2 and ResNet-101 models. Previous work in this area has typically focused on classifying a limited number of activities using PPG data, making our results, which were obtained using two different datasets and real-life settings, particularly encouraging.Item Analysis and modeling of lithium iron phosphate (LiFePO4) batteries for state of health estimation(COMSATS University Islamabad Lahore Campus, 2022) Hamza Bashir,; FA19-REE-016; , Assistant Profesor [Supervisor]Li-ion batteries have been chosen as the best candidate for portable, mobile and high power applications due to their high energy densities, long lifetimes, and high efficiencies compared to other battery types, like lead-acid and nickel-based batteries recently. On the other hand, these batteries are prone to failure due to charge imbalance in batteries linked in series or parallel, which may be catastrophic. Therefore, they must be carefully monitored in real-time. So, the Li-ion cells chemistry batteries have exceptional performance compared to other chemistries, but only if treated well (cell balancing, protection from overcharge, over-discharge, and short circuit conditions). Despite the advantages of lithium-ion batteries, these batteries still have a short life due to particular aging processes inside the batteries and improper cell balancing, which become evident under defined conditions. Reliable battery calendar ageing prediction, on the other hand, remains a crucial yet complex issue for improving the performance of related storage devices. So, manufacturers and researchers need to investigate the cell balancing process and aging processes in lithium-ion batteries to determine the aging effects that occur and the factors that cause a rapid decrease in their lifetimes. Nowadays, Lithium iron phosphate (LiFePO4) based chemistry batteries are considered to be one of the most valuable lithium-ion batteries in the market because of their high energy density, lack of memory effect, lower self-discharge, long lifetime, large cycle life number, inherently safe cathode structure under critical conditions, and non polluting characteristics. In this thesis, 3S-1P battery pack cell balancing system is implemented by using passive cell balancing technique including the thermal effects and an aging prediction model has been developed for lithium iron phosphate (LiFePO4) batteries using MATLAB/Simulink©, which has been able to predict the aged effect in terms of capacity fade and internal resistance increase. Thevenin equivalent circuit model is utilized and SOH is estimated. Parameter estimation is done using soft computing technique. Overall, different models of SOH estimations are studied and reviewed. The results are developed in MATLAB Simscape and further extended in MATLAB scripts. Possible effects of cell balancing and internal resistance has been discussed in the conclusion.Item Blood glucose forecasting in type 1 diabetes mellitus patients using machine learning techniques(comsats university Islamabad lahore campus, 2022) Hatim Butt,; FA19-REE-009; LHR TP 7889Diabetes Mellitus is a metabolic disease that causes the body to lose control over blood glucose regulation. Patients with Type 1 diabetes completely rely on insulin therapy by themselves or using some automated insulin delivery systems. In both the cases, it is pertinent to have good estimate of future blood glucose levels. An efficient diabetes management demands accurate prediction of future blood glucose levels, failure of which results in short and long term health complications. With the modern exordium of quantified-self such as continuous glucose monitoring(CGM) systems, a patient can have access to their personalized glycemic profile which can be utilized for accurate prediction of future blood glucose levels. In recent years, machine learning methodologies have sparked a lot of interest in predicting glucose levels in diabetic patients, leading to the development of a variety of methods and techniques. However, the prediction accuracies of these methods are not good enough to be declared them as reliable predictors for evaluating glycemic conditions. In this research work we utilized multi-layered Long Short Term Memory(LSTM) network a famous deep learning technique based on recurrent neural network(RNN) for making prediction of blood glucose levels in patients with type 1 diabetes. The proposed framework predicts the future blood glucose level using Ohio T1DM dataset at prediction horizon(PH) of 30 and 60 minutes. Experimentation was also carried out on better feature representation to model in order to achieve higher prediction accuracy. The effect of different input feature sets, towards improvement of prediction accuracy was also been investigated. The results on Ohio T1DM Dataset (2018), that contain eight weeks’ worth of data shows that our method achieves the lowest RMSE score of 14.76mg/dL and 25.48mg/dL for prediction horizon of 30min and 60min respectively. The obtained results are the best known as per our knowledge using this dataset. The proposed methodology can be utilized in closed loop systems for precise insulin delivery to patient for their better glycemic control.Item Efficient tracking of subatomic particles in the presence of multiple scattering in muon ionization cooling experiment(comsats university islamabad lahore campus, 2022) Tehreem Tariq; FA19-REE-005; Dr. Imran Ghous; LHR TP 7888Muon Ionization Cooling Experiment (MICE) is an experiment of high-energy physics. It is used to cool muon beams using ionization cooling. The target of MICE is to obtain a muon beam with reduced phase space. Particle detection inside the cooling chamber is important to gain insight into the process. It is a challenging task because millions of particles pass through the particle detectors, and it becomes further complicated due to the presence of multiple scattering. When particles pass through the detectors, multiple scattering occurs. Multiple scattering distorts the trajectories of particles. As a result, the position and momentum of particles change, and the non-linearity of the system increases. Track fitting algorithms are used for particle tracking in MICE. In the past, Kalman Filters (KFs) and Particle Filters (PFs) and their adaptations have been used for non-linear tracking in MICE experiments in the absence of multiple scattering. PF has been widely used in non-linear systems, but it was not used for non-linear non-Gaussian multiple scattering in MICE. Ignoring the multiple scattering or taking it as a Gaussian approximation, PF outperformed Unscented Kalman Filter (UKF) and Unscented Particle Filter (UPF) when compared in terms of accuracy but was more computationally complex than UKF. UPF was more computationally complex than PF but solved the problem of particle degeneracy and shifted the particles to the regions of high likelihood. In this thesis, non-linear non-Gaussian multiple scattering is studied by using the filters, which perform well for non-linear non-Gaussian systems because multiple scattering is non-Gaussian in nature, whereas most of the filtering techniques are designed for Gaussian approximations. The effect of multiple scattering, in its true form, on performance the of MICE has not been studied before. This will be the contribution of this research. In this research, UKF, PF, and UPF are used to study the behavior of the system in the presence of non-Gaussian multiple scattering and to compare their effectiveness in terms of accuracy and computational complexity.Item Unscented Kalman Filter Observer-Based Model(COMSATS University Islamabad Lahore Campus, 2022) Syeda Farwa Fatima; , FA19-REE-008; Dr. Muhammad Jawad, Assistant Profesor [Supervisor]; LHR TP 7605A doubly-fed induction generator (DFIG) is among the primary actuators for wind energy generation due to merits, such as low converter cost, controllable power factor, reduced power losses, variable speed operation, constant grid frequency, maximal wind energy production, and active/reactive power control. The control and estimation problems of the DFIGs are of prime importance to be accurately and properly investigated. The recent advancement in microprocessor technology has led to the implementation of more sophisticated and reliable control schemes such as direct power control, direct torque control, sliding mode control and predictive control. For the DFIGs, the conventional control system often lacks to attain satisfactory results during the transient period due to nonlinearity and a highly coupled control system. Finite control set model predictive control (FCS-MPC) seems to be a very promising solution to control the active and reactive power and to regulate the switching states of rotor side converter due to its flexibility in defining the control objectives, improved dynamic performance and constraint handling. Apart from the inherently non-linear nature of the DFIG, what makes the problem particularly challenging is the unavailability of the measurements such as rotor speed and position. The use of sensors for determining the rotor speed and position not only increases the size, hardware complexity and maintenance cost of the DFIG systems but also decreases the system robustness. The researchers have developed various sensorless algorithms such as unscented Kalman filter, extended Kalman filter, model reference adaptive system, sliding mode observer, luenberger observer for the estimation of parameters. The previous algorithms are less efficient in a way that some of them cause inadequate results for highly non-linear systems and others are unable to operate in the low-speed range. The unscented Kalman filter (UKF) comes to the rescue to deal with the aforementioned issues and generate better results by the estimation of the parameters without linearization. This thesis presents a novel hybrid technique of Finite Control Set Model Predictive Control (FCS-MPC) with Unscented Kalman Filter (UKF) to a challenging control and estimation problem of DFIG. The proposed technique deals with the inherently non linear nature of DFIG and the unavailability of the measurements such as rotor speed and position. FCS-MPC is used for regulating the switching states of the rotor side converter. UKF is selected as an observer to estimate the dynamic states of DFIG.Item Anomaly Detection And Short Term Forecasting(Publisher COMSATS University Islambad Lahore Campus, 2021) Taimoor Muzaffar Gondal,; FA19-REE-012; Dr. Ikramullah Khosa, Assistant Profesor [Supervisor]; LHR TP 7473Forecasting of Electric load always had an empirical impact on power operations. It is the most challenging and critical attribute of energy management system planning and operation. With the emergence of smart grid (SG) technology, the conventional grid has been promoted to a modern digital grid which ensures robustness, reliability, safety, and economic viability to the existing power systems. In contrast, it has introduced complex information and greater threats for electric load forecasting. As a result, cybersecurity has become a major challenge for the existing grid. Cyber attacks have numerous types but the most important one is the data integrity attack which results to reflect wrong projections. The data integrity attacks are termed as anomalies in time series data. Such anomalies can malignantly reshape a specific segment of input load data, which results in a remarkable deviation from original values. In this thesis, an anomaly detection model and a short-term load forecasting based model have been developed. Initially, anomaly detection has been implemented on electrical load for the data of duration three years. The LSTM model is trained and used to predict the electrical load and the mean square error is computed on test data. Based on maximum observed error, a threshold is selected to detect anomalies in the total data. After anomaly detection, the short-term load forecasting is performed using LSTM and Bi-LSTM methods and the results are compared on the basis of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Additionally, the computational complexity analysis for both algorithms is carried out. For short-term forecasting, hourly data of electrical load is considered. The Bi-LSTM model produced the MAPE of 8.79% while simple LSTM model showed 14.85%. The Bi-LSTM outperformed the conventional LSTM model with a margin of 6% MAPE overall. However, the computational time of LSTM model was short, almost 60% of the time taken by the Bi-LSTM model.